It is important in the therapy of tumor diseases to check the success of the therapy by regular progress examinations, in order to decide in good time whether a therapy is to be continued or not or whether a change of therapy would be more sensible. In the estimation of the therapy success, however, as well as establishing the size of the tumorous tissue, the determination of the proportion of necrotic tissue in the tumor volume is of importance, since tumors react differently to different forms of therapy and do not necessarily change their shape, but merely die off partly or completely, i.e. necrotize.
There is also the danger with biopsies of a suspected lesion that only tissue which is already dead, i.e. necrotic tissue, will be removed with the biopsy needle, which is not suitable for a further examination. Information about the geometric location of the vital or the necrotic proportion of a tumor is also valuable in carrying out the biopsies, in order to ensure that vital tissue is removed.
In a previously usual method to enable the necrotic proportion of tumor tissue to be determined, a contrast medium examination is introduced using a single-source computed tomograph. With such an examination two recordings or two scans are carried out. The first of the two recordings is a native recording made without contrast medium, while at least one second recording is made after introduction of the contrast medium, mostly iodine. The contrast medium temporarily accumulates in the tumor, wherein the necrotic tissue does not take up any contrast medium, since it is dead. Frequently a number of images are recorded after the addition of contrast medium, even a number of images in different accumulation phases in the tissue, for example in an arterial, a venous and in a late venous phase, in order in this way to obtain additional information about the vital tissue by its iodine take-up behavior.
To determine the necrotic tissue a differentiation could be carried out on the basis of image datasets obtained by these measurements. To do this the values of the respective pixels of the two image datasets are subtracted from one another. A pixel is to be understood below as a voxel or pixel depending on whether three-dimensional or two-dimensional image data is involved. Within the context of the invention the values of the pixel accordingly generally involve intensity values, such as Hounsfield values (unit HU), which are a measure of the attenuation of the x-rays arising at this pixel. However this subtraction method requires an exact alignment and adaptation of the recorded tissue sections or of the tumor tissue in the different image datasets to one another. Since a certain period of time elapses between the two recordings, there can be patient movements and/or deformations of the tumor tissue as a result of the often soft tissue structure in the intervening period. These tissue displacements can in fact mostly be compensated for in a non-rigid, i.e. elastic registration. However these types of methods require large computer capacities and unfortunately also do not always deliver straightforward results. Thus this method is hardly ever used in clinical practice.